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10 papers · 2 filters
Convolutional Neural Networks at Constrained Time Cost
Kaiming He, Jian Sun
Though recent advanced convolutional neural networks (CNNs) have been improving the image recognition accuracy, the models are getting more complex and time-consuming. For real-wor…
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov +3
Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting.…
Actions and Attributes from Wholes and Parts
Georgia Gkioxari, Ross Girshick, Jitendra Malik
We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired…
Efficient and Accurate Approximations of Nonlinear Convolutional Networks
Xiangyu Zhang, Jianhua Zou, Xiang Ming +2
This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs). Unlike existing methods that are designed for approximating linear filters or…
Hypercolumns for Object Segmentation and Fine-grained Localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick +1
Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation. However, the information in this layer may be to…
CIDEr: Consensus-based Image Description Evaluation
Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh
Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attri…